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Record W2344086365 · doi:10.1108/qmr-07-2014-0056

Brand adoption by BoP retailers

2017· article· en· W2344086365 on OpenAlexaff
Piyush Kumar Sinha, Suraksha Gupta, Saurabh Rawal

Bibliographic record

VenueQualitative Market Research An International Journal · 2017
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicInnovation and Socioeconomic Development
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsBusinessOriginalityProfitability indexMarketingBrand awarenessBrand extensionBrand managementBottom of the pyramidValue (mathematics)AdvertisingBrand equityQualitative research

Abstract

fetched live from OpenAlex

Purpose The purpose of this paper is to develop a framework to understand how bottom of the pyramid (BoP) retailers adopt brands who sell to a very different set of customers and are served through long indirect channels. Design/methodology/approach In this study, 60 retailers belonging to different villages of Central and North Gujarat were interviewed. The interviews were audio recorded, transcribed and analyzed. A grounded theory-based analysis was carried out. Findings The analysis brought out six criteria used by the retailers in selecting brands with demand for the brand as the most dominant factor. Other criteria included brand adoption by other retailers, profitability, influence of wholesaler/distributor and packaging. Originality/value Previous studies with regard to brand adoption by retailers have focused on large retailers who are approached directly by the brands. There is a lack of studies on how BoP retailers adopt brands. Most studies have approached the subject from a distribution perspective of reaching these markets.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.009
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies, Scholarly communication, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.405
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0090.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0040.004
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0050.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.171
GPT teacher head0.477
Teacher spread0.306 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations18
Published2017
Admission routes1
Has abstractyes

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